文献类型：期刊文献

中文题名：The Comparison of Spatial Interpolation Methods on Temperature and Precipitation of Sanjiangyuan Area

英文题名：三江源地区温度和降水量空间插值方法比较(英文)

作者：彭红兰[1] 刘芳[1] 朵海瑞[1] 李迪强[1]

第一作者：彭红兰

机构：[1]中国林科院森林生态环境与保护研究所,北京100091

年份：2010

卷号：1

期号：5

起止页码：7-11

中文期刊名：Meteorological and Environmental Research

外文期刊名：气象与环境研究（英文版）

基金：Supported by Forestry Science and Technology Support Project (2008BADB0B0203);National Technology Support Project (2007BAC03A08-5)

语种：中文

中文关键词：Sanjiangyuan area; Interpolation; COK; TPS; China

外文关键词：Sanjiangyuan area; Interpolation; COK; TPS; China

分类号：P468.02

摘要：In order to get the spatial grid data of monthly precipitation and monthly average temperature of Sanjiangyuan area, the Co-Kriging (COK) and thin plate smoothing splines(TPS) interpolation methods were applied by using the climate data during 1971-2000 of 58 meteorological stations around Qinghai Province and the 3 arc-second digital elevation model (DEM) data. The performance was evaluated by the smallest statistical errors by general cross validation (GCV). Root-mean-squared predicted errors (RMSE) and mean absolute errors (MAE) were used to compare the performance of the two methods. The results showed that: 1) After combing covariates into the models, both methods performed better; 2) The performance of TPS was significantly better than COK: for monthly average temperature, the RMSE derived from TPS was 69.48% higher than COK, as MAE increased by 70.56%. And for monthly precipitation, the RMSE derived from TPS was 28.07% higher than COK, as MAE increased by 29.06%.
In order to get the spatial grid data of monthly precipitation and monthly average temperature of Sanjiangyuan area, the Co-Kriging (COK) and thin plate smoothing splines(TPS) interpolation methods were applied by using the climate data during 1971-2000 of 58 meteorological stations around Qinghai Province and the 3 arc-second digital elevation model (DEM) data. The performance was evaluated by the smallest statistical errors by general cross validation (GCV). Root-mean-squared predicted errors (RMSE) and mean absolute errors (MAE) were used to compare the performance of the two methods. The results showed that: 1) After combing covariates into the models, both methods performed better; 2) The performance of TPS was significantly better than COK: for monthly average temperature, the RMSE derived from TPS was 69.48% higher than COK, as MAE increased by 70.56%. And for monthly precipitation, the RMSE derived from TPS was 28.07% higher than COK, as MAE increased by 29.06%.